Data scientists

Building a sustainable data pipeline is critical to accurate machine learning models. Learn how Explorium supports you at every point in the process.

In this blog post, we look at the key questions you need to ask to make sure you’re using the data preparation tools you really need.

See how machine learning in retail can help you build better inventory management systems.

In this article, we explain how to get ready for predictive model deployment, from preparing data pipelines to retraining ML models.

With so much data in your own stores, it’s tempting to think you have all you need to start producing great predictive insights. This might be The post What Is Augmented Data Discovery with...

Your machine learning model is only as good as the data you feed into it. That makes data preparation (or cleaning, wrangling, cleansing, pre-processing, or any The post Top Tips for Data...

In this in-depth article, we explain how the right questions will help you get the domain knowledge you need for data science for business.

Data privacy continues to be a major hurdle for risk officers. In this article, we explain how the global increase in data surveillance creates short term opportunities but long term risks.

To succeed in the data science field, you need more than just technical acumen. We reveal the secrets to making your team indispensable.

When it comes to external data for machine learning, data catalogs provide a handful of time-saving benefits over databases. Learn more.

In this article, we explain how to turn the biggest data science challenges of the moment into business opportunities.

Data marketplaces make the lives of data scientists looking for machine learning datasets much easier. Read how.

In this article, we explain where external data comes from, the key challenges, and how to connect and utilize it with data science solutions.

With ML models rendered useless, we built an entirely new set of COVID-19 signals in our platform that let organizations understand their risk derived from the current pandemic.

Let’s take a look at some of the most prevalent types of bias, the data mistakes that cause them – and how to prevent this from happening in your own models.